Instructions to use Kaspar/vit-base-railspace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaspar/vit-base-railspace with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kaspar/vit-base-railspace") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Kaspar/vit-base-railspace") model = AutoModelForImageClassification.from_pretrained("Kaspar/vit-base-railspace", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 412702e72c791cbff82167a9a3cd0126ea958edd3c304284131924dbb2f16a79
- Size of remote file:
- 343 MB
- SHA256:
- 34b92ef91574278b206b51dab811e19b8ac6f473c56de6832b417dc2ed6d09eb
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